Application of maximum power point tracking based on meta-heuristic optimization algorithm in photovoltaic systems with battery storage

The increasing global energy demand and the objectives of environmental sustainability have made the efficient utilization of renewable energy sources imperative. Renewable energy source photovoltaic systems, are considered one of the most prominent technologies in energy production. However, external factors such as fluctuations in solar irradiance and partial shading directly affect the efficiency of photovoltaic systems. Therefore, maximum power point tracking algorithms have been developed to ensure that PV systems operate at their maximum power point under all conditions. Additionally, the effective use of energy storage systems is of great importance for ensuring continuity and power quality during the grid integration of such systems. In this thesis, a grid-connected PV-BESS was designed, and both MPPT and battery energy management were implemented. The generated power is directed to the battery group via a bidirectional DC-DC converter, and in case of need, power is transferred from the battery to the grid through three-phase inverter. A 100 kW grid-connected PV-BESS system was modeled in the Matlab/Simulink environment. The modeled system maintains stable operation under varying shading conditions and transfers high-quality energy to the grid with a very low total harmonic distortion level of 0.89%. Furthermore, a high MPPT efficiency of 99.88% was achieved using the hybrid RFO-P&O algorithm. Thus, a comprehensive and effective solution has been proposed for the reliable and sustainable integration of photovoltaic-based systems into the electrical grid.